1. Field of the Invention
The present invention relates to the field of speech recognition and, more particularly, to the detection of commands in continuous speech.
2. Description of the Background Art
Command spotting systems, which are responsive to human voice, are highly desirable for a wide variety of consumer products. In a telecommunications instrument, for example, typical operations such as on/off, transmit/receive, volume, push-button dialing, speech recognizer training, and telephone answering device functions may be readily achieved by monitoring an audio input channel and taking appropriate action whenever a specific utterance (the command) appears in the input. For each command to be recognized by the system, a statistical model such as, for example, a template or hidden Markov model (HMM) well known in the art, is maintained. The statistical model defines the likelihood that a given segment of input contains a command utterance.
During its operation, a conventional command spotting system continually generates conjectures or hypotheses about the identities and locations of command words in the currently observed input. Each hypothesis is tested against a respective command model and a score is generated for its respective likelihood. The score may be determined by, for example, conventional Viterbi scoring. If the score exceeds a threshold. T, the hypothesis is considered as accepted and the action associated with it is effected. Otherwise, the hypothesis is rejected. The probability distribution of the score of either a correct or a false hypothesis depends on a variety of factors, including the speaker, the transducer, and the acoustical environment. A fixed threshold T is usually set sufficiently high to ensure, for the maximum number of users, an acceptably low false alarm rate over the whole range of expected operating conditions. Unfortunately, due to wide variations in user voice characteristics and environmental conditions, the selected threshold typically functions much better for some users than others.
Users having a low probability of exceeding the threshold may, on a regular basis, be ignored by the system. One technique for addressing the problem of frequently rejected users is directed to reducing the threshold level. Setting the threshold too low, however, typically results in an unacceptably high number of false positive hypotheses for average users.